Physical activity, cardiorespiratory fitness, and cardiovascular health: A clinical practice statement of the American Society for Preventive Cardiology Part II: Physical activity, cardiorespiratory fitness, minimum and goal intensities for exercise training, prescriptive methods, and special patient populations

Franklin, Barry A.; Eijsvogels, Thijs M.H.; Pandey, Ambarish; Quindry, John C.; Tóth, Peter P.

doi:10.1016/j.ajpc.2022.100425

Cited by 34 publications

(24 citation statements)

References 170 publications

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“… 17 Bursts of physical activity as short as one to two minutes, accumulated over time, can produce cardiovascular benefits. 23 As such, people could engage in periodic bursts of inefficient walking, perhaps lasting only a few minutes at a time, at times and places that are most convenient for them. In fact, inefficient walking can be performed entirely indoors.…”

Section: Discussionmentioning

confidence: 99%

“…But it is important to note that the EE of exercise is the same whether performed all at one time or distributed throughout the day,22 and cardiovascular benefits similarly accrue when daily recommended amounts of exercise are completed in multiple short bouts 17. Bursts of physical activity as short as one to two minutes, accumulated over time, can produce cardiovascular benefits 23. As such, people could engage in periodic bursts of inefficient walking, perhaps lasting only a few minutes at a time, at times and places that are most convenient for them.…”

Section: Discussionmentioning

confidence: 99%

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Quantifying the benefits of inefficient walking: Monty Python inspired laboratory based experimental study

Gaesser

Poole

Angadi

2022

BMJ

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Objective To compare the rate of energy expenditure of low efficiency walking with high efficiency walking. Design Laboratory based experimental study. Setting United States. Participants 13 healthy adults (six women, seven men) with no known gait disorder, mean (±standard deviation) age 34.2±16.1 years, height 174.2±12.6 cm, weight 78.2±22.5 kg, and body mass index 25.6±6.0. Intervention Participants performed three, five minute walking trials around an indoor 30 m course. The first trial consisted of walking at a freely chosen walking speed in the participant’s usual style. The next two trials consisted of low efficiency walks in which participants were asked to duplicate the walks of Mr Teabag and Mr Putey (acted by John Cleese and Michael Palin, respectively) in the legendary Monty Python Ministry of Silly Walks (MoSW) skit that first aired in 1970. Distance covered during the five minute walks was used to calculate average speed. Ventilation and gas exchange were collected throughout to determine oxygen uptake (V̇O 2 ; mL O 2 /kg/min) and energy expenditure (EE; kcal/kg/min; 1 kcal=4.18 kJ), reported as mean±standard deviation. Main outcome measures V̇O 2 and EE. Results V̇O 2 and EE were about 2.5 times higher (P<0.001) during the Teabag walk compared with participants’ usual walk (27.9±4.8 v 11.3±1.9 mL O 2 /kg/min; 0.14±0.03 v 0.06±0.01 kcal/kg/min), but were not different during the Putey walk (12.3±1.8 mL/kg/min; 0.06±0.01 kcal/kg/min). Each minute of Teabag walking increased EE over participants’ usual walking by an average of 8.0 kcal (range 5.5-12.0) in men and by 5.2 kcal (range 3.9-6.2) in women, and qualified as vigorous intensity physical activity (>6 resting metabolic equivalents). Conclusions For adults with no known gait disorder who average approximately 5000 steps/day, exchanging about 22%-34% of their daily steps with higher energy, low efficiency walking in Teabag style—requiring around 12-19 min—could increase daily EE by 100 kcal. Adults could achieve 75 minutes of vigorous intensity physical activity per week by walking inefficiently for about 11 min/day. Had an initiative to promote inefficient movement been adopted in the early 1970s, we might now be living among a healthier society. Efforts to promote higher energy—and perhaps more joyful—walking should ensure inclusivity and inefficiency for all.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantifying the benefits of inefficient walking: Monty Python inspired laboratory based experimental study

Gaesser

Poole

Angadi

2022

BMJ

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“…1 Indeed, over recent decades cardiorespiratory fitness (CRF) has emerged as the strongest independent predictor of all-cause and CVD-related mortality, with every 1 MET (Metabolic equivalent of task -VO 2 : 3.5 mL•kg −1 •min −1 ) increase in fitness being associated with a ~16% reduction in mortality. 2 Moreover, the addition of CRF to traditional risk factors significantly improves the sensitivity of risk classification for adverse health outcomes in asymptomatic individuals and independently predicts survival rates in both low-and highrisk populations. 3 Readers of this journal will also be encouraged by the emerging evidence that supports the inclusion of CRF in the pre-operative risk assessment of patients undergoing major intra-abdominal surgery.…”

Section: E D I T O R I a L Editorial: Prime Time To Recommend Sex-spe...mentioning

confidence: 99%

“…There is now irrefutable evidence that regular moderate‐to‐vigorous physical activity elicits multifaceted and systematic health benefits that offer potent protection against chronic disease, cardiovascular events, early hospitalisation and premature death 1 . Indeed, over recent decades cardiorespiratory fitness (CRF) has emerged as the strongest independent predictor of all‐cause and CVD‐related mortality, with every 1 MET (Metabolic equivalent of task ‐ VO 2 : 3.5 mL·kg −1 ·min −1 ) increase in fitness being associated with a ~16% reduction in mortality 2 . Moreover, the addition of CRF to traditional risk factors significantly improves the sensitivity of risk classification for adverse health outcomes in asymptomatic individuals and independently predicts survival rates in both low‐ and high‐risk populations 3 .…”

Section: Figurementioning

confidence: 99%

Editorial: Prime time to recommend sex‐specific cardiorespiratory fitness threshold values for pre‐operative risk stratification

Pugh

Williams

2023

Eur J Clin Investigation

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“…Individually prescribed physical activity should be part of the daily routine of everyone and is an indispensable part of rehabilitation programs. 8 Machine learning (ML) approaches to predictive modeling have gained increasing attention in recent years. Myers et al 9 employed a neural network (NN), a type of ML algorithm, to improve the prediction of cardiovascular death from exercise test responses in patients with heart failure.…”

mentioning

confidence: 99%

A Machine Learning Approach to Developing an Accurate Prediction of Maximal Heart Rate During Exercise Testing in Apparently Healthy Adults

Cundrič

Bosnić

Kaminsky

et al. 2023

Journal of Cardiopulmonary Rehabilitation and Prevention

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Maximal heart rate (HR max ) continues to be an important measure of adequate effort during an exercise test. The aim of this study was to improve the accuracy of HR max prediction using a machine learning (ML) approach. Methods: We used a sample from the Fitness Registry of the Importance of Exercise National Database, which included 17 325 apparently healthy individuals (81% males) who performed a maximal cardiopulmonary exercise test. Two standard formulas for HR max prediction were tested: Formula1 = 220 − age (yr), root-mean-squared error (RMSE) 21.9, relative root-mean-squared error (RRMSE) 1.1; and Formula2 = 209.3 − 0.72 × age (yr), RMSE 22.7 and RRMSE 1.1. For ML model prediction, we used age, weight, height, resting HR, and systolic and diastolic blood pressure. The following ML algorithms to predict HR max were applied: lasso regression (LR), neural networks (NN), support vector machine (SVM) and random forests (RF). An evaluation was performed using cross-validation and by computing the RMSE and RRMSE, Pearson correlation, and Bland-Altman plots. The best predictive model was explained with Shapley Additive Explanations (SHAP). Results: The HR max for the cohort was 162 ± 20 bpm. All ML models improved HR max prediction and reduced RMSE and RRMSE compared with Formula1 (LR: 20.2%, NN: 20.4%, SVM: 22.2%, and RF: 24.7%). The predictions of all algorithms significantly correlated with HR max (r = 0.49, 0.51, 0.54, 0.57, respectively; P < .001). Bland-Altman analysis demonstrated lower bias and 95% CI for all ML models in comparison with standard equations. The SHAP explanation showed a high impact of all selected variables. Conclusions: Machine learning, particularly the RF model, improved prediction of HR max using readily available measures. This approach should be considered for clinical application to refine HR max prediction.

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Cited by 34 publications

References 170 publications

Quantifying the benefits of inefficient walking: Monty Python inspired laboratory based experimental study

Quantifying the benefits of inefficient walking: Monty Python inspired laboratory based experimental study

Editorial: Prime time to recommend sex‐specific cardiorespiratory fitness threshold values for pre‐operative risk stratification

A Machine Learning Approach to Developing an Accurate Prediction of Maximal Heart Rate During Exercise Testing in Apparently Healthy Adults

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